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Fg Data Profiling

Fg Data Profiling is an open-source Python library that analyzes Pandas and Spark DataFrames for exploratory data analysis and potential data-quality issues. It produces reports with dataset summaries, inferred column types, statistics, visualizations, and alerts, with output options including HTML, JSON, and Jupyter Notebook widgets.

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Independent OpenViST project profile, based on linked sources.

Fg Data Profiling — OpenViST project profile in AI Data & Analytics.
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Understand the project

Practical overview

When it fits

Use it during dataset intake or exploratory analysis when you want a repeatable summary of a Pandas or Spark DataFrame before deciding what to investigate or address. It can also add a profiling step to Python data workflows.

Who it is for

  • Data analysts who need a structured first look at a dataset.
  • Python practitioners adding profiling reports to Pandas or Spark data workflows.
  • Teams that want HTML reports for review or JSON output for automated systems.

Documented uses

  • Summarize a DataFrame's structure, size, missingness, and duplicate rows.
  • Inspect distributions, descriptive statistics, correlations, and potential quality issues.
  • Analyze time-series or text columns as part of exploratory analysis.
  • Export profiling results as HTML or JSON, or display them in a Jupyter Notebook.
  • Compare datasets through a profiling report.
Before you choose

Requirements & limits

The repository identifies the project as open source under the MIT license. Installation instructions are provided for pip and conda.

  • Requires Python 3 and a Python environment; the library is used from code rather than as a standalone web app.
  • Spark support is provided through the pyspark extra.
  • The documented report formats include HTML and JSON, with an additional Jupyter Notebook widget option.
  • The project documents a rename from ydata-profiling to fg-data-profiling; existing imports may need updating.
  • The project describes YData Fabric as a separate option for database and storage connections.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the renamed package and update imports

Move a project from the old ydata-profiling package name to fg-data-profiling.

Before you start

  • Python 3
  • An existing project that uses ydata-profiling
  • pip

Steps

  1. Uninstall the old package with the command below.

    Documented command
    pip uninstall ydata-profiling

  2. Install the new package with the command below.

    Documented command
    pip install fg-data-profiling

  3. Replace ydata_profiling imports with data_profiling; for example, import ProfileReport from data_profiling.

  4. Use grep -r "ydata_profiling" . --include="*.py" to find affected Python files.

Expected outcome

The environment uses fg-data-profiling and project imports use the documented new module name.

Guide limitations

  • This guide covers the documented package and import migration, not execution of a profiling workflow.
  • The old package is stated to no longer receive updates or bug fixes.
Documentation 1 ↗
Source-based practical guide

Generate a Pandas profiling report

Create a standard profiling report from a Pandas DataFrame.

Before you start

  • Python 3
  • pip
  • A Pandas DataFrame

Steps

  1. Install the package with the command below.

    Documented command
    pip install fg-data-profiling

  2. Load or create a Pandas DataFrame in Python.

  3. Import ProfileReport from data_profiling.

  4. Create a report with ProfileReport(df, title="Profiling Report").

Expected outcome

A profiling report object is created for the DataFrame; the library documents HTML and JSON export formats.

Guide limitations

  • The quickstart documents report creation but does not specify an export command.
  • Spark profiling requires the PySpark extra rather than this Pandas workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
2 October 2026
Original website title
Data-Centric-AI-Community/fg-data-profiling · Official homepage title · retained in its source language
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